caiyulamp's starred repositories
SpeedyWeather.jl
Play atmospheric modelling like it's LEGO.
Ocean-Wave-Prediction-Model-with-LSTM
Deep learning application for predicting ocean wave behaviors.
ConvLSTM_pytorch
Implementation of Convolutional LSTM in PyTorch.
ConvLSTM-PyTorch
ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
SwitchHosts
Switch hosts quickly!
qt-creator
A cross-platform Qt IDE
Oceananigans.jl
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Dependencies
A rewrite of the old legacy software "depends.exe" in C# for Windows devs to troubleshoot dll load dependencies issues.
giddy-2020-roammiz
Analysis following Giddy et al - 2020, Stirring of sea ice meltwater enhances submesoscale fronts in the Southern Ocean
Synthetic-SAR-Image-Generation-for-Oil-Spill-Detection
Synthetic-SAR-Image-Generation-for-Oil-Spill-Detection" utilizes synthetic aperture radar to detect oil spills. By generating synthetic SAR images and applying advanced object detection, it enhances monitoring and response to oil spill incidents.
Oill-Spill-Detection
Oil Spill Detection Algorithm using Convolutional Neural Network Architecture : U-net
Oil_Spill_Detection
Object Recognition using MobileNetV2 and Segmentation using U-net
U-NET-Image-Segmentation
Practical use case of U-NET model for Segmentation of Images to detect Oil leakge
OIL-SPILL-DETECTION-ON-SEA-SURFACE-USING-SAR-IMAGES
Pollution by oil spills in open sea and coastal waters, whether accidental or deliberate, is a major problem, due to frequent transport of goods by ships, and represents a serious threat to the marine environment. Identification of an oil spill is essential to evaluate the potential spread and float from the source to the adjacent coastal terrains. Usage of the Synthetic Aperture RADAR (SAR) information for recognition and checking of oil spills has got extensive consideration because of their wide area inclusion, day-night and all-weather capabilities. In existing system, they use some sensor to detect oil. But it is not easy for every time. And it doesn’t have accurate output. To overcome that we proposed new system by using MATLAB. The present examination studies an oil spill occurred in some regions by applying Sentinel 1 SAR-C images. Approaches dependent on MATLAB images examination have been produced for distinguishing oil spills from referred to common leaks just as oil slick procedures. In this work, Oil spill is located on the ocean/sea using YOLO algorithm with MATLAB. In this project, one of these techniques is associated with Sentinel 1 images of a known region of natural oil leakage and of an ongoing oil slick in incident zone. The Synthetic Aperture Radar (SAR) is perceived as the most significant remote sensing apparatus for the ocean and ocean waters oil slick examination and propagation. The results will give better outputs when compared to existing works.
MatlabOilspilldetection
Oil spill detection in Synthetic Aperture Radar (SAR) images is a vital tool in monitoring and mitigating the environmental impact of oil spills on water bodies.
CV_Fish_Classificarion-Pytorch
a small ML/CV to classify 8 classes of common fish types with 93%+ accuracy
predrnn-pytorch
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.
ST-LSTM_PyTorch
ST-LSTM network implemented using PyTorch.
Two-stage-CNN-LSTM-
Learning the spatio-temporal relationship between wind and significant wave height using deep learning
copernicusmarine-feedstock
A conda-smithy repository for copernicusmarine.